Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
an be used to abstract away from the physical reality by describing it as components that exist in discrete states with probabilistically invoked actions that change the state. The...
Duncan Gillies, David Thornley, Chatschik Bisdikia...
The structure of a Bayesian network (BN) encodes variable independence. Learning the structure of a BN, however, is typically of high computational complexity. In this paper, we e...
Microprocessor designers use techniques such as clock gating to reduce power dissipation. An unfortunate side-effect of these techniques is the processor current fluctuations th...
We describe a technique for automatically proving compiler optimizations sound, meaning that their transformations are always semantics-preserving. We first present a domainspeci...